The ability to learn recursive predicates is fundamental in learning a variety of algorithmic tasks ([1]}, [2]}). In practice, Metagol is the only notable ILP solver which can efficiently learn recursive predicates (via meta-rule templates).
Our evaluationsMany of the symbolic tasks used in [3]} as well as some others are provided in the accompanying source code. show that the proposed dNL-ILP solver can learn a variety of discrete algorithmic tasks involving recursion very efficiently and without the need for predefined meta-rules. Here, we briefly explore two synthetic learning tasks before considering large-scale tasks involving relational datasets.
